Predicting discomfort scores reported by LHD operators using whole-body vibration exposure values and musculoskeletal pain scores
Bibliographic record
Abstract
BACKGROUND: Laboratory studies have typically been used to establish a relationship between whole body vibration (WBV) magnitude, frequency, exposure duration and reported discomfort. However, the relationship between subjective reports of discomfort, and predicted comfort response according to International Standards Organization (ISO) 2631-1, predicted health risks according to ISO 2631-1 and reported musculoskeletal discomfort has not been examined. The purpose here was to compare discomfort values predicted by ISO 2631-1 with the subjective discomfort reported by nine Load-haul-dump (LHD) operators during typical operating conditions. METHODS: Vibration exposure at the operator/seat interface was measured and processed, for one-hour duration, according to criteria established in ISO 2631-1. Vibration total values were determined for 1-minute exposure periods and the LHD operators provided a discomfort score associated with the same vibration exposure period. A linear regression analysis and correlation was carried out to determine the strength of the relationship between the predicted subjective reports of discomfort, ISO 2631-1 discomfort, objectively measured acceleration levels and reported musculoskeletal discomfort. FINDINGS: Reported discomfort was poorly correlated to ISO discomfort scores (r=0.1799). Vibration exposure values and Musculo-Skeletal Disorder (MSD) variables were related to both ISO 2631-1 discomfort and to reported discomfort. The MSD scores produced stronger relationships with reported discomfort scores than did the vibration exposure values.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".